Overview

Dataset statistics

Number of variables34
Number of observations148670
Missing cells181135
Missing cells (%)3.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory38.6 MiB
Average record size in memory272.0 B

Variable types

Numeric11
Categorical23

Alerts

year has constant value "2019"Constant
Gender is highly overall correlated with co-applicant_credit_typeHigh correlation
Interest_rate_spread is highly overall correlated with Secured_by and 4 other fieldsHigh correlation
Secured_by is highly overall correlated with Interest_rate_spread and 2 other fieldsHigh correlation
Security_Type is highly overall correlated with Interest_rate_spread and 2 other fieldsHigh correlation
Status is highly overall correlated with Interest_rate_spread and 1 other fieldsHigh correlation
Upfront_charges is highly overall correlated with construction_type and 1 other fieldsHigh correlation
business_or_commercial is highly overall correlated with loan_typeHigh correlation
co-applicant_credit_type is highly overall correlated with GenderHigh correlation
construction_type is highly overall correlated with Interest_rate_spread and 3 other fieldsHigh correlation
credit_type is highly overall correlated with StatusHigh correlation
income is highly overall correlated with loan_amount and 1 other fieldsHigh correlation
loan_amount is highly overall correlated with income and 1 other fieldsHigh correlation
loan_type is highly overall correlated with business_or_commercialHigh correlation
open_credit is highly overall correlated with Upfront_chargesHigh correlation
property_value is highly overall correlated with income and 1 other fieldsHigh correlation
rate_of_interest is highly overall correlated with Interest_rate_spreadHigh correlation
loan_limit is highly imbalanced (63.9%)Imbalance
Credit_Worthiness is highly imbalanced (74.6%)Imbalance
open_credit is highly imbalanced (96.4%)Imbalance
Neg_ammortization is highly imbalanced (52.5%)Imbalance
interest_only is highly imbalanced (72.3%)Imbalance
lump_sum_payment is highly imbalanced (84.3%)Imbalance
construction_type is highly imbalanced (99.7%)Imbalance
occupancy_type is highly imbalanced (72.9%)Imbalance
Secured_by is highly imbalanced (99.7%)Imbalance
total_units is highly imbalanced (93.6%)Imbalance
Security_Type is highly imbalanced (99.7%)Imbalance
loan_limit has 3344 (2.2%) missing valuesMissing
rate_of_interest has 36439 (24.5%) missing valuesMissing
Interest_rate_spread has 36639 (24.6%) missing valuesMissing
Upfront_charges has 39642 (26.7%) missing valuesMissing
property_value has 15098 (10.2%) missing valuesMissing
income has 9150 (6.2%) missing valuesMissing
LTV has 15098 (10.2%) missing valuesMissing
dtir1 has 24121 (16.2%) missing valuesMissing
LTV is highly skewed (γ1 = 120.6153375)Skewed
ID is uniformly distributedUniform
ID has unique valuesUnique
Upfront_charges has 20770 (14.0%) zerosZeros

Reproduction

Analysis started2024-06-11 05:22:26.383816
Analysis finished2024-06-11 05:23:44.674679
Duration1 minute and 18.29 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct148670
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99224.5
Minimum24890
Maximum173559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:44.754606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum24890
5-th percentile32323.45
Q162057.25
median99224.5
Q3136391.75
95-th percentile166125.55
Maximum173559
Range148669
Interquartile range (IQR)74334.5

Descriptive statistics

Standard deviation42917.477
Coefficient of variation (CV)0.43252903
Kurtosis-1.2
Mean99224.5
Median Absolute Deviation (MAD)37167.5
Skewness0
Sum1.4751706 × 1010
Variance1.8419098 × 109
MonotonicityStrictly increasing
2024-06-11T08:23:44.917631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24890 1
 
< 0.1%
123979 1
 
< 0.1%
123999 1
 
< 0.1%
124000 1
 
< 0.1%
124001 1
 
< 0.1%
124002 1
 
< 0.1%
124003 1
 
< 0.1%
124004 1
 
< 0.1%
124005 1
 
< 0.1%
124006 1
 
< 0.1%
Other values (148660) 148660
> 99.9%
ValueCountFrequency (%)
24890 1
< 0.1%
24891 1
< 0.1%
24892 1
< 0.1%
24893 1
< 0.1%
24894 1
< 0.1%
24895 1
< 0.1%
24896 1
< 0.1%
24897 1
< 0.1%
24898 1
< 0.1%
24899 1
< 0.1%
ValueCountFrequency (%)
173559 1
< 0.1%
173558 1
< 0.1%
173557 1
< 0.1%
173556 1
< 0.1%
173555 1
< 0.1%
173554 1
< 0.1%
173553 1
< 0.1%
173552 1
< 0.1%
173551 1
< 0.1%
173550 1
< 0.1%

year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2019
148670 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters594680
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 148670
100.0%

Length

2024-06-11T08:23:45.074808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:45.189374image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2019 148670
100.0%

Most occurring characters

ValueCountFrequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 594680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 594680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 594680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 148670
25.0%
0 148670
25.0%
1 148670
25.0%
9 148670
25.0%

loan_limit
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing3344
Missing (%)2.2%
Memory size1.1 MiB
cf
135348 
ncf
 
9978

Length

Max length3
Median length2
Mean length2.0686594
Min length2

Characters and Unicode

Total characters300630
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcf
2nd rowcf
3rd rowcf
4th rowcf
5th rowcf

Common Values

ValueCountFrequency (%)
cf 135348
91.0%
ncf 9978
 
6.7%
(Missing) 3344
 
2.2%

Length

2024-06-11T08:23:45.310085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:45.426330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
cf 135348
93.1%
ncf 9978
 
6.9%

Most occurring characters

ValueCountFrequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 300630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 300630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 300630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 145326
48.3%
f 145326
48.3%
n 9978
 
3.3%

Gender
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Male
42346 
Joint
41399 
Sex Not Available
37659 
Female
27266 

Length

Max length17
Median length6
Mean length7.9382391
Min length4

Characters and Unicode

Total characters1180178
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSex Not Available
2nd rowMale
3rd rowMale
4th rowMale
5th rowJoint

Common Values

ValueCountFrequency (%)
Male 42346
28.5%
Joint 41399
27.8%
Sex Not Available 37659
25.3%
Female 27266
18.3%

Length

2024-06-11T08:23:45.548893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:45.669675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
male 42346
18.9%
joint 41399
18.5%
sex 37659
16.8%
not 37659
16.8%
available 37659
16.8%
female 27266
12.2%

Most occurring characters

ValueCountFrequency (%)
e 172196
14.6%
l 144930
12.3%
a 144930
12.3%
o 79058
 
6.7%
i 79058
 
6.7%
t 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
J 41399
 
3.5%
n 41399
 
3.5%
Other values (8) 280486
23.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1180178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 172196
14.6%
l 144930
12.3%
a 144930
12.3%
o 79058
 
6.7%
i 79058
 
6.7%
t 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
J 41399
 
3.5%
n 41399
 
3.5%
Other values (8) 280486
23.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1180178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 172196
14.6%
l 144930
12.3%
a 144930
12.3%
o 79058
 
6.7%
i 79058
 
6.7%
t 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
J 41399
 
3.5%
n 41399
 
3.5%
Other values (8) 280486
23.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1180178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 172196
14.6%
l 144930
12.3%
a 144930
12.3%
o 79058
 
6.7%
i 79058
 
6.7%
t 79058
 
6.7%
75318
 
6.4%
M 42346
 
3.6%
J 41399
 
3.5%
n 41399
 
3.5%
Other values (8) 280486
23.8%

approv_in_adv
Categorical

Distinct2
Distinct (%)< 0.1%
Missing908
Missing (%)0.6%
Memory size1.1 MiB
nopre
124621 
pre
23141 

Length

Max length5
Median length5
Mean length4.6867801
Min length3

Characters and Unicode

Total characters692528
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownopre
2nd rownopre
3rd rowpre
4th rownopre
5th rowpre

Common Values

ValueCountFrequency (%)
nopre 124621
83.8%
pre 23141
 
15.6%
(Missing) 908
 
0.6%

Length

2024-06-11T08:23:45.821071image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:45.950619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
nopre 124621
84.3%
pre 23141
 
15.7%

Most occurring characters

ValueCountFrequency (%)
p 147762
21.3%
r 147762
21.3%
e 147762
21.3%
n 124621
18.0%
o 124621
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 692528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 147762
21.3%
r 147762
21.3%
e 147762
21.3%
n 124621
18.0%
o 124621
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 692528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 147762
21.3%
r 147762
21.3%
e 147762
21.3%
n 124621
18.0%
o 124621
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 692528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 147762
21.3%
r 147762
21.3%
e 147762
21.3%
n 124621
18.0%
o 124621
18.0%

loan_type
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
type1
113173 
type2
20762 
type3
14735 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters743350
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtype1
2nd rowtype2
3rd rowtype1
4th rowtype1
5th rowtype1

Common Values

ValueCountFrequency (%)
type1 113173
76.1%
type2 20762
 
14.0%
type3 14735
 
9.9%

Length

2024-06-11T08:23:46.070272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:46.189157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
type1 113173
76.1%
type2 20762
 
14.0%
type3 14735
 
9.9%

Most occurring characters

ValueCountFrequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 743350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 743350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 743350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 148670
20.0%
y 148670
20.0%
p 148670
20.0%
e 148670
20.0%
1 113173
15.2%
2 20762
 
2.8%
3 14735
 
2.0%

loan_purpose
Categorical

Distinct4
Distinct (%)< 0.1%
Missing134
Missing (%)0.1%
Memory size1.1 MiB
p3
55934 
p4
54799 
p1
34529 
p2
 
3274

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters297072
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowp1
2nd rowp1
3rd rowp1
4th rowp4
5th rowp1

Common Values

ValueCountFrequency (%)
p3 55934
37.6%
p4 54799
36.9%
p1 34529
23.2%
p2 3274
 
2.2%
(Missing) 134
 
0.1%

Length

2024-06-11T08:23:46.317708image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:46.435404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
p3 55934
37.7%
p4 54799
36.9%
p1 34529
23.2%
p2 3274
 
2.2%

Most occurring characters

ValueCountFrequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 148536
50.0%
3 55934
 
18.8%
4 54799
 
18.4%
1 34529
 
11.6%
2 3274
 
1.1%

Credit_Worthiness
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
l1
142344 
l2
 
6326

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters297340
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowl1
2nd rowl1
3rd rowl1
4th rowl1
5th rowl1

Common Values

ValueCountFrequency (%)
l1 142344
95.7%
l2 6326
 
4.3%

Length

2024-06-11T08:23:46.565495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:46.677195image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
l1 142344
95.7%
l2 6326
 
4.3%

Most occurring characters

ValueCountFrequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 148670
50.0%
1 142344
47.9%
2 6326
 
2.1%

open_credit
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
nopc
148114 
opc
 
556

Length

Max length4
Median length4
Mean length3.9962602
Min length3

Characters and Unicode

Total characters594124
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownopc
2nd rownopc
3rd rownopc
4th rownopc
5th rownopc

Common Values

ValueCountFrequency (%)
nopc 148114
99.6%
opc 556
 
0.4%

Length

2024-06-11T08:23:46.797243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:46.912694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
nopc 148114
99.6%
opc 556
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 594124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 594124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 594124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 148670
25.0%
p 148670
25.0%
c 148670
25.0%
n 148114
24.9%

business_or_commercial
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
nob/c
127908 
b/c
20762 

Length

Max length5
Median length5
Mean length4.7206968
Min length3

Characters and Unicode

Total characters701826
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownob/c
2nd rowb/c
3rd rownob/c
4th rownob/c
5th rownob/c

Common Values

ValueCountFrequency (%)
nob/c 127908
86.0%
b/c 20762
 
14.0%

Length

2024-06-11T08:23:47.043550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:47.173858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
nob/c 127908
86.0%
b/c 20762
 
14.0%

Most occurring characters

ValueCountFrequency (%)
b 148670
21.2%
/ 148670
21.2%
c 148670
21.2%
n 127908
18.2%
o 127908
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 701826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 148670
21.2%
/ 148670
21.2%
c 148670
21.2%
n 127908
18.2%
o 127908
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 701826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 148670
21.2%
/ 148670
21.2%
c 148670
21.2%
n 127908
18.2%
o 127908
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 701826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 148670
21.2%
/ 148670
21.2%
c 148670
21.2%
n 127908
18.2%
o 127908
18.2%

loan_amount
Real number (ℝ)

HIGH CORRELATION 

Distinct211
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331117.74
Minimum16500
Maximum3576500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:47.307214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum16500
5-th percentile106500
Q1196500
median296500
Q3436500
95-th percentile656500
Maximum3576500
Range3560000
Interquartile range (IQR)240000

Descriptive statistics

Standard deviation183909.31
Coefficient of variation (CV)0.55541968
Kurtosis9.1277753
Mean331117.74
Median Absolute Deviation (MAD)120000
Skewness1.6669981
Sum4.9227275 × 1010
Variance3.3822634 × 1010
MonotonicityNot monotonic
2024-06-11T08:23:47.480038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206500 4610
 
3.1%
256500 4079
 
2.7%
156500 3967
 
2.7%
226500 3944
 
2.7%
486500 3819
 
2.6%
306500 3691
 
2.5%
246500 3669
 
2.5%
216500 3649
 
2.5%
236500 3553
 
2.4%
266500 3543
 
2.4%
Other values (201) 110146
74.1%
ValueCountFrequency (%)
16500 3
 
< 0.1%
26500 27
 
< 0.1%
36500 119
 
0.1%
46500 212
 
0.1%
56500 810
 
0.5%
66500 859
 
0.6%
76500 1701
1.1%
86500 1605
1.1%
96500 1484
1.0%
106500 3210
2.2%
ValueCountFrequency (%)
3576500 1
 
< 0.1%
3346500 1
 
< 0.1%
3006500 4
< 0.1%
2986500 1
 
< 0.1%
2926500 1
 
< 0.1%
2706500 1
 
< 0.1%
2626500 1
 
< 0.1%
2606500 1
 
< 0.1%
2596500 1
 
< 0.1%
2506500 2
< 0.1%

rate_of_interest
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct131
Distinct (%)0.1%
Missing36439
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean4.0454758
Minimum0
Maximum8
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:47.741726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.125
Q13.625
median3.99
Q34.375
95-th percentile4.99
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.56139119
Coefficient of variation (CV)0.13877013
Kurtosis0.34456404
Mean4.0454758
Median Absolute Deviation (MAD)0.365
Skewness0.38840603
Sum454027.8
Variance0.31516007
MonotonicityNot monotonic
2024-06-11T08:23:47.910480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.99 14455
 
9.7%
3.625 8800
 
5.9%
3.875 8592
 
5.8%
3.75 8474
 
5.7%
3.5 6866
 
4.6%
4.5 6809
 
4.6%
4.375 6482
 
4.4%
4.25 6045
 
4.1%
4.125 5797
 
3.9%
4.75 4875
 
3.3%
Other values (121) 35036
23.6%
(Missing) 36439
24.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
2.125 1
 
< 0.1%
2.25 4
 
< 0.1%
2.375 2
 
< 0.1%
2.475 2
 
< 0.1%
2.5 21
< 0.1%
2.575 1
 
< 0.1%
2.6 3
 
< 0.1%
2.625 25
< 0.1%
2.65 2
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7.75 1
 
< 0.1%
7.5 2
 
< 0.1%
7.375 1
 
< 0.1%
7.125 1
 
< 0.1%
7 1
 
< 0.1%
6.875 1
 
< 0.1%
6.75 5
< 0.1%
6.5 3
< 0.1%
6.375 1
 
< 0.1%

Interest_rate_spread
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22516
Distinct (%)20.1%
Missing36639
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean0.44165566
Minimum-3.638
Maximum3.357
Zeros9
Zeros (%)< 0.1%
Negative21883
Negative (%)14.7%
Memory size1.1 MiB
2024-06-11T08:23:48.069302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-3.638
5-th percentile-0.31745
Q10.076
median0.3904
Q30.7754
95-th percentile1.3794
Maximum3.357
Range6.995
Interquartile range (IQR)0.6994

Descriptive statistics

Standard deviation0.51304274
Coefficient of variation (CV)1.1616351
Kurtosis-0.18356608
Mean0.44165566
Median Absolute Deviation (MAD)0.3427
Skewness0.28076233
Sum49479.125
Variance0.26321285
MonotonicityNot monotonic
2024-06-11T08:23:48.237772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.028 77
 
0.1%
-0.038 64
 
< 0.1%
-0.023 60
 
< 0.1%
-0.173 56
 
< 0.1%
-0.148 52
 
< 0.1%
0.252 51
 
< 0.1%
0.202 51
 
< 0.1%
0.257 46
 
< 0.1%
0.112 46
 
< 0.1%
-0.013 45
 
< 0.1%
Other values (22506) 111483
75.0%
(Missing) 36639
 
24.6%
ValueCountFrequency (%)
-3.638 1
< 0.1%
-1.0841 1
< 0.1%
-1.047 1
< 0.1%
-1.0462 1
< 0.1%
-1.039 1
< 0.1%
-1.038 1
< 0.1%
-1.0379 1
< 0.1%
-1.0343 1
< 0.1%
-1.0294 1
< 0.1%
-1.0288 1
< 0.1%
ValueCountFrequency (%)
3.357 1
< 0.1%
2.8854 1
< 0.1%
2.7227 1
< 0.1%
2.6368 1
< 0.1%
2.5932 1
< 0.1%
2.5851 1
< 0.1%
2.537 1
< 0.1%
2.5144 1
< 0.1%
2.4093 1
< 0.1%
2.182 1
< 0.1%

Upfront_charges
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct58271
Distinct (%)53.4%
Missing39642
Missing (%)26.7%
Infinite0
Infinite (%)0.0%
Mean3224.9961
Minimum0
Maximum60000
Zeros20770
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:48.411611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1581.49
median2596.45
Q34812.5
95-th percentile9272.6885
Maximum60000
Range60000
Interquartile range (IQR)4231.01

Descriptive statistics

Standard deviation3251.1215
Coefficient of variation (CV)1.0081009
Kurtosis6.3685863
Mean3224.9961
Median Absolute Deviation (MAD)2108.66
Skewness1.7540757
Sum3.5161488 × 108
Variance10569791
MonotonicityNot monotonic
2024-06-11T08:23:48.582935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20770
 
14.0%
1250 1184
 
0.8%
1150 892
 
0.6%
795 487
 
0.3%
295 403
 
0.3%
950 192
 
0.1%
3000 173
 
0.1%
995 151
 
0.1%
4000 149
 
0.1%
5000 147
 
0.1%
Other values (58261) 84480
56.8%
(Missing) 39642
26.7%
ValueCountFrequency (%)
0 20770
14.0%
0.03 1
 
< 0.1%
0.06 1
 
< 0.1%
0.35 1
 
< 0.1%
0.6 1
 
< 0.1%
0.72 1
 
< 0.1%
0.75 1
 
< 0.1%
0.92 1
 
< 0.1%
1 12
 
< 0.1%
1.15 1
 
< 0.1%
ValueCountFrequency (%)
60000 1
< 0.1%
53485.78 1
< 0.1%
38437.5 1
< 0.1%
38375 1
< 0.1%
37604.38 1
< 0.1%
35192.5 1
< 0.1%
33268 1
< 0.1%
32850 1
< 0.1%
32825.25 1
< 0.1%
32647 1
< 0.1%

term
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing41
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean335.13658
Minimum96
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:48.741825image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum96
5-th percentile180
Q1360
median360
Q3360
95-th percentile360
Maximum360
Range264
Interquartile range (IQR)0

Descriptive statistics

Standard deviation58.409084
Coefficient of variation (CV)0.17428442
Kurtosis3.1732363
Mean335.13658
Median Absolute Deviation (MAD)0
Skewness-2.1748218
Sum49811015
Variance3411.621
MonotonicityNot monotonic
2024-06-11T08:23:48.890894image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
360 121685
81.8%
180 12981
 
8.7%
240 5859
 
3.9%
300 2822
 
1.9%
324 2766
 
1.9%
120 510
 
0.3%
144 263
 
0.2%
348 260
 
0.2%
336 213
 
0.1%
96 194
 
0.1%
Other values (16) 1076
 
0.7%
ValueCountFrequency (%)
96 194
 
0.1%
108 33
 
< 0.1%
120 510
 
0.3%
132 93
 
0.1%
144 263
 
0.2%
156 174
 
0.1%
165 1
 
< 0.1%
168 82
 
0.1%
180 12981
8.7%
192 17
 
< 0.1%
ValueCountFrequency (%)
360 121685
81.8%
348 260
 
0.2%
336 213
 
0.1%
324 2766
 
1.9%
322 1
 
< 0.1%
312 185
 
0.1%
300 2822
 
1.9%
288 90
 
0.1%
280 1
 
< 0.1%
276 100
 
0.1%

Neg_ammortization
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing121
Missing (%)0.1%
Memory size1.1 MiB
not_neg
133420 
neg_amm
15129 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1039843
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot_neg
2nd rownot_neg
3rd rowneg_amm
4th rownot_neg
5th rownot_neg

Common Values

ValueCountFrequency (%)
not_neg 133420
89.7%
neg_amm 15129
 
10.2%
(Missing) 121
 
0.1%

Length

2024-06-11T08:23:49.042885image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:49.165018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
not_neg 133420
89.8%
neg_amm 15129
 
10.2%

Most occurring characters

ValueCountFrequency (%)
n 281969
27.1%
_ 148549
14.3%
e 148549
14.3%
g 148549
14.3%
o 133420
12.8%
t 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1039843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 281969
27.1%
_ 148549
14.3%
e 148549
14.3%
g 148549
14.3%
o 133420
12.8%
t 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1039843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 281969
27.1%
_ 148549
14.3%
e 148549
14.3%
g 148549
14.3%
o 133420
12.8%
t 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1039843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 281969
27.1%
_ 148549
14.3%
e 148549
14.3%
g 148549
14.3%
o 133420
12.8%
t 133420
12.8%
m 30258
 
2.9%
a 15129
 
1.5%

interest_only
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
not_int
141560 
int_only
 
7110

Length

Max length8
Median length7
Mean length7.047824
Min length7

Characters and Unicode

Total characters1047800
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot_int
2nd rownot_int
3rd rownot_int
4th rownot_int
5th rownot_int

Common Values

ValueCountFrequency (%)
not_int 141560
95.2%
int_only 7110
 
4.8%

Length

2024-06-11T08:23:49.309893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:49.432157image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
not_int 141560
95.2%
int_only 7110
 
4.8%

Most occurring characters

ValueCountFrequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1047800
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1047800
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1047800
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 297340
28.4%
t 290230
27.7%
o 148670
14.2%
_ 148670
14.2%
i 148670
14.2%
l 7110
 
0.7%
y 7110
 
0.7%

lump_sum_payment
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
not_lpsm
145286 
lpsm
 
3384

Length

Max length8
Median length8
Mean length7.9089527
Min length4

Characters and Unicode

Total characters1175824
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot_lpsm
2nd rowlpsm
3rd rownot_lpsm
4th rownot_lpsm
5th rownot_lpsm

Common Values

ValueCountFrequency (%)
not_lpsm 145286
97.7%
lpsm 3384
 
2.3%

Length

2024-06-11T08:23:49.577676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:49.714048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
not_lpsm 145286
97.7%
lpsm 3384
 
2.3%

Most occurring characters

ValueCountFrequency (%)
l 148670
12.6%
p 148670
12.6%
s 148670
12.6%
m 148670
12.6%
n 145286
12.4%
o 145286
12.4%
t 145286
12.4%
_ 145286
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1175824
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 148670
12.6%
p 148670
12.6%
s 148670
12.6%
m 148670
12.6%
n 145286
12.4%
o 145286
12.4%
t 145286
12.4%
_ 145286
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1175824
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 148670
12.6%
p 148670
12.6%
s 148670
12.6%
m 148670
12.6%
n 145286
12.4%
o 145286
12.4%
t 145286
12.4%
_ 145286
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1175824
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 148670
12.6%
p 148670
12.6%
s 148670
12.6%
m 148670
12.6%
n 145286
12.4%
o 145286
12.4%
t 145286
12.4%
_ 145286
12.4%

property_value
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct385
Distinct (%)0.3%
Missing15098
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean497893.47
Minimum8000
Maximum16508000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:49.852542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile148000
Q1268000
median418000
Q3628000
95-th percentile1058000
Maximum16508000
Range16500000
Interquartile range (IQR)360000

Descriptive statistics

Standard deviation359935.32
Coefficient of variation (CV)0.72291633
Kurtosis73.221196
Mean497893.47
Median Absolute Deviation (MAD)170000
Skewness4.5862758
Sum6.6504626 × 1010
Variance1.2955343 × 1011
MonotonicityNot monotonic
2024-06-11T08:23:50.025338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
308000 2792
 
1.9%
258000 2763
 
1.9%
358000 2679
 
1.8%
408000 2537
 
1.7%
328000 2524
 
1.7%
278000 2513
 
1.7%
268000 2497
 
1.7%
228000 2493
 
1.7%
238000 2408
 
1.6%
288000 2398
 
1.6%
Other values (375) 107968
72.6%
(Missing) 15098
 
10.2%
ValueCountFrequency (%)
8000 6
 
< 0.1%
18000 1
 
< 0.1%
28000 9
 
< 0.1%
38000 35
 
< 0.1%
48000 71
 
< 0.1%
58000 141
 
0.1%
68000 271
0.2%
78000 387
0.3%
88000 568
0.4%
98000 556
0.4%
ValueCountFrequency (%)
16508000 1
< 0.1%
12008000 1
< 0.1%
11008000 1
< 0.1%
10008000 1
< 0.1%
9268000 1
< 0.1%
8508000 1
< 0.1%
7608000 1
< 0.1%
6908000 1
< 0.1%
6508000 1
< 0.1%
6408000 1
< 0.1%

construction_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
sb
148637 
mh
 
33

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters297340
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsb
2nd rowsb
3rd rowsb
4th rowsb
5th rowsb

Common Values

ValueCountFrequency (%)
sb 148637
> 99.9%
mh 33
 
< 0.1%

Length

2024-06-11T08:23:50.182763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:50.300231image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
sb 148637
> 99.9%
mh 33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 148637
50.0%
b 148637
50.0%
m 33
 
< 0.1%
h 33
 
< 0.1%

occupancy_type
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
pr
138201 
ir
 
7340
sr
 
3129

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters297340
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpr
2nd rowpr
3rd rowpr
4th rowpr
5th rowpr

Common Values

ValueCountFrequency (%)
pr 138201
93.0%
ir 7340
 
4.9%
sr 3129
 
2.1%

Length

2024-06-11T08:23:50.430187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:50.551667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
pr 138201
93.0%
ir 7340
 
4.9%
sr 3129
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 148670
50.0%
p 138201
46.5%
i 7340
 
2.5%
s 3129
 
1.1%

Secured_by
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
home
148637 
land
 
33

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters594680
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhome
2nd rowhome
3rd rowhome
4th rowhome
5th rowhome

Common Values

ValueCountFrequency (%)
home 148637
> 99.9%
land 33
 
< 0.1%

Length

2024-06-11T08:23:50.678360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:50.798573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
home 148637
> 99.9%
land 33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 594680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 594680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 594680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 148637
25.0%
o 148637
25.0%
m 148637
25.0%
e 148637
25.0%
l 33
 
< 0.1%
a 33
 
< 0.1%
n 33
 
< 0.1%
d 33
 
< 0.1%

total_units
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1U
146480 
2U
 
1477
3U
 
393
4U
 
320

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters297340
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1U
2nd row1U
3rd row1U
4th row1U
5th row1U

Common Values

ValueCountFrequency (%)
1U 146480
98.5%
2U 1477
 
1.0%
3U 393
 
0.3%
4U 320
 
0.2%

Length

2024-06-11T08:23:51.009862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:51.131289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1u 146480
98.5%
2u 1477
 
1.0%
3u 393
 
0.3%
4u 320
 
0.2%

Most occurring characters

ValueCountFrequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 297340
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 148670
50.0%
1 146480
49.3%
2 1477
 
0.5%
3 393
 
0.1%
4 320
 
0.1%

income
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1001
Distinct (%)0.7%
Missing9150
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean6957.3389
Minimum0
Maximum578580
Zeros1260
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:51.270066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1920
Q13720
median5760
Q38520
95-th percentile15420
Maximum578580
Range578580
Interquartile range (IQR)4800

Descriptive statistics

Standard deviation6496.5864
Coefficient of variation (CV)0.93377461
Kurtosis885.29246
Mean6957.3389
Median Absolute Deviation (MAD)2280
Skewness17.307695
Sum9.7068792 × 108
Variance42205635
MonotonicityNot monotonic
2024-06-11T08:23:51.440786image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1260
 
0.8%
3600 1250
 
0.8%
4200 1243
 
0.8%
4800 1191
 
0.8%
3120 1168
 
0.8%
3720 1161
 
0.8%
3900 1159
 
0.8%
5400 1152
 
0.8%
3300 1144
 
0.8%
4500 1139
 
0.8%
Other values (991) 127653
85.9%
(Missing) 9150
 
6.2%
ValueCountFrequency (%)
0 1260
0.8%
60 5
 
< 0.1%
120 12
 
< 0.1%
180 12
 
< 0.1%
240 15
 
< 0.1%
300 18
 
< 0.1%
360 11
 
< 0.1%
420 15
 
< 0.1%
480 11
 
< 0.1%
540 17
 
< 0.1%
ValueCountFrequency (%)
578580 1
< 0.1%
377220 1
< 0.1%
374400 1
< 0.1%
335880 2
< 0.1%
329460 1
< 0.1%
322860 1
< 0.1%
312000 1
< 0.1%
240000 1
< 0.1%
235980 1
< 0.1%
198060 1
< 0.1%

credit_type
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
CIB
48152 
CRIF
43901 
EXP
41319 
EQUI
15298 

Length

Max length4
Median length3
Mean length3.3981906
Min length3

Characters and Unicode

Total characters505209
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXP
2nd rowEQUI
3rd rowEXP
4th rowEXP
5th rowCRIF

Common Values

ValueCountFrequency (%)
CIB 48152
32.4%
CRIF 43901
29.5%
EXP 41319
27.8%
EQUI 15298
 
10.3%

Length

2024-06-11T08:23:51.599558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:51.743882image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
cib 48152
32.4%
crif 43901
29.5%
exp 41319
27.8%
equi 15298
 
10.3%

Most occurring characters

ValueCountFrequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 505209
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 505209
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 505209
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 107351
21.2%
C 92053
18.2%
E 56617
11.2%
B 48152
9.5%
R 43901
8.7%
F 43901
8.7%
X 41319
 
8.2%
P 41319
 
8.2%
Q 15298
 
3.0%
U 15298
 
3.0%

Credit_Score
Real number (ℝ)

Distinct401
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean699.7891
Minimum500
Maximum900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:51.913999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile519
Q1599
median699
Q3800
95-th percentile881
Maximum900
Range400
Interquartile range (IQR)201

Descriptive statistics

Standard deviation115.87586
Coefficient of variation (CV)0.16558683
Kurtosis-1.2026494
Mean699.7891
Median Absolute Deviation (MAD)100
Skewness0.004766757
Sum1.0403765 × 108
Variance13427.214
MonotonicityNot monotonic
2024-06-11T08:23:52.104303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
763 415
 
0.3%
867 413
 
0.3%
639 411
 
0.3%
581 408
 
0.3%
554 407
 
0.3%
519 406
 
0.3%
737 406
 
0.3%
890 406
 
0.3%
687 405
 
0.3%
617 405
 
0.3%
Other values (391) 144588
97.3%
ValueCountFrequency (%)
500 357
0.2%
501 357
0.2%
502 346
0.2%
503 383
0.3%
504 392
0.3%
505 379
0.3%
506 380
0.3%
507 386
0.3%
508 400
0.3%
509 348
0.2%
ValueCountFrequency (%)
900 393
0.3%
899 352
0.2%
898 370
0.2%
897 383
0.3%
896 391
0.3%
895 371
0.2%
894 361
0.2%
893 348
0.2%
892 366
0.2%
891 376
0.3%

co-applicant_credit_type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
CIB
74392 
EXP
74278 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters446010
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCIB
2nd rowEXP
3rd rowCIB
4th rowCIB
5th rowEXP

Common Values

ValueCountFrequency (%)
CIB 74392
50.0%
EXP 74278
50.0%

Length

2024-06-11T08:23:52.272641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:52.403994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
cib 74392
50.0%
exp 74278
50.0%

Most occurring characters

ValueCountFrequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 446010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 446010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 446010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 74392
16.7%
I 74392
16.7%
B 74392
16.7%
E 74278
16.7%
X 74278
16.7%
P 74278
16.7%

age
Categorical

Distinct7
Distinct (%)< 0.1%
Missing200
Missing (%)0.1%
Memory size1.1 MiB
45-54
34720 
35-44
32818 
55-64
32534 
65-74
20744 
25-34
19142 
Other values (2)
8512 

Length

Max length5
Median length5
Mean length4.8853371
Min length3

Characters and Unicode

Total characters725326
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-34
2nd row55-64
3rd row35-44
4th row45-54
5th row25-34

Common Values

ValueCountFrequency (%)
45-54 34720
23.4%
35-44 32818
22.1%
55-64 32534
21.9%
65-74 20744
14.0%
25-34 19142
12.9%
>74 7175
 
4.8%
<25 1337
 
0.9%
(Missing) 200
 
0.1%

Length

2024-06-11T08:23:52.560367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:52.717491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
45-54 34720
23.4%
35-44 32818
22.1%
55-64 32534
21.9%
65-74 20744
14.0%
25-34 19142
12.9%
74 7175
 
4.8%
25 1337
 
0.9%

Most occurring characters

ValueCountFrequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 725326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 725326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 725326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 214671
29.6%
5 208549
28.8%
- 139958
19.3%
6 53278
 
7.3%
3 51960
 
7.2%
7 27919
 
3.8%
2 20479
 
2.8%
> 7175
 
1.0%
< 1337
 
0.2%
Distinct2
Distinct (%)< 0.1%
Missing200
Missing (%)0.1%
Memory size1.1 MiB
to_inst
95814 
not_inst
52656 

Length

Max length8
Median length7
Mean length7.3546575
Min length7

Characters and Unicode

Total characters1091946
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowto_inst
2nd rowto_inst
3rd rowto_inst
4th rownot_inst
5th rownot_inst

Common Values

ValueCountFrequency (%)
to_inst 95814
64.4%
not_inst 52656
35.4%
(Missing) 200
 
0.1%

Length

2024-06-11T08:23:52.876177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:53.004341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
to_inst 95814
64.5%
not_inst 52656
35.5%

Most occurring characters

ValueCountFrequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1091946
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1091946
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1091946
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 296940
27.2%
n 201126
18.4%
o 148470
13.6%
_ 148470
13.6%
i 148470
13.6%
s 148470
13.6%

LTV
Real number (ℝ)

MISSING  SKEWED 

Distinct8484
Distinct (%)6.4%
Missing15098
Missing (%)10.2%
Infinite0
Infinite (%)0.0%
Mean72.746457
Minimum0.9674782
Maximum7831.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:53.150542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.9674782
5-th percentile36.350575
Q160.47486
median75.13587
Q386.184211
95-th percentile98.728814
Maximum7831.25
Range7830.2825
Interquartile range (IQR)25.70935

Descriptive statistics

Standard deviation39.967603
Coefficient of variation (CV)0.54940961
Kurtosis19979.045
Mean72.746457
Median Absolute Deviation (MAD)12.514733
Skewness120.61534
Sum9716889.8
Variance1597.4093
MonotonicityNot monotonic
2024-06-11T08:23:53.333345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.25 530
 
0.4%
91.66666667 499
 
0.3%
80.03875969 380
 
0.3%
80.03246753 328
 
0.2%
94.95614035 322
 
0.2%
78.84615385 317
 
0.2%
78.64583333 310
 
0.2%
79.04040404 309
 
0.2%
80.06329114 309
 
0.2%
95.16806723 306
 
0.2%
Other values (8474) 129962
87.4%
(Missing) 15098
 
10.2%
ValueCountFrequency (%)
0.967478198 1
< 0.1%
2.072942643 1
< 0.1%
2.767587397 1
< 0.1%
2.81374502 1
< 0.1%
2.856420627 1
< 0.1%
2.992584746 1
< 0.1%
3.083554377 1
< 0.1%
3.125 1
< 0.1%
3.74668435 1
< 0.1%
3.875171468 1
< 0.1%
ValueCountFrequency (%)
7831.25 1
< 0.1%
6706.25 1
< 0.1%
5206.25 1
< 0.1%
4706.25 1
< 0.1%
2956.25 1
< 0.1%
2331.25 1
< 0.1%
263.5416667 1
< 0.1%
237.5 2
< 0.1%
220.3629032 1
< 0.1%
201.7857143 1
< 0.1%

Region
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
North
74722 
south
64016 
central
8697 
North-East
 
1235

Length

Max length10
Median length5
Mean length5.1585323
Min length5

Characters and Unicode

Total characters766919
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsouth
2nd rowNorth
3rd rowsouth
4th rowNorth
5th rowNorth

Common Values

ValueCountFrequency (%)
North 74722
50.3%
south 64016
43.1%
central 8697
 
5.8%
North-East 1235
 
0.8%

Length

2024-06-11T08:23:53.500486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:53.635287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
north 74722
50.3%
south 64016
43.1%
central 8697
 
5.8%
north-east 1235
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
c 8697
 
1.1%
e 8697
 
1.1%
Other values (4) 19864
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 766919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
c 8697
 
1.1%
e 8697
 
1.1%
Other values (4) 19864
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 766919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
c 8697
 
1.1%
e 8697
 
1.1%
Other values (4) 19864
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 766919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 149905
19.5%
o 139973
18.3%
h 139973
18.3%
r 84654
11.0%
N 75957
9.9%
s 65251
8.5%
u 64016
8.3%
a 9932
 
1.3%
c 8697
 
1.1%
e 8697
 
1.1%
Other values (4) 19864
 
2.6%

Security_Type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
direct
148637 
Indriect
 
33

Length

Max length8
Median length6
Mean length6.0004439
Min length6

Characters and Unicode

Total characters892086
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdirect
2nd rowdirect
3rd rowdirect
4th rowdirect
5th rowdirect

Common Values

ValueCountFrequency (%)
direct 148637
> 99.9%
Indriect 33
 
< 0.1%

Length

2024-06-11T08:23:53.790967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:53.931941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
direct 148637
> 99.9%
indriect 33
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 892086
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 892086
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 892086
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 148670
16.7%
i 148670
16.7%
r 148670
16.7%
e 148670
16.7%
c 148670
16.7%
t 148670
16.7%
I 33
 
< 0.1%
n 33
 
< 0.1%

Status
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
112031 
1
36639 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters148670
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 112031
75.4%
1 36639
 
24.6%

Length

2024-06-11T08:23:54.065700image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-11T08:23:54.200953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 148670
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 148670
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112031
75.4%
1 36639
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 148670
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112031
75.4%
1 36639
 
24.6%

dtir1
Real number (ℝ)

MISSING 

Distinct57
Distinct (%)< 0.1%
Missing24121
Missing (%)16.2%
Infinite0
Infinite (%)0.0%
Mean37.732932
Minimum5
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-06-11T08:23:54.348668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q131
median39
Q345
95-th percentile54
Maximum61
Range56
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.545435
Coefficient of variation (CV)0.27947563
Kurtosis0.37888256
Mean37.732932
Median Absolute Deviation (MAD)7
Skewness-0.55146496
Sum4699599
Variance111.2062
MonotonicityNot monotonic
2024-06-11T08:23:54.610764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 6848
 
4.6%
36 6553
 
4.4%
44 6500
 
4.4%
49 6309
 
4.2%
43 5307
 
3.6%
42 5121
 
3.4%
41 4881
 
3.3%
40 4699
 
3.2%
39 4540
 
3.1%
38 4461
 
3.0%
Other values (47) 69330
46.6%
(Missing) 24121
 
16.2%
ValueCountFrequency (%)
5 386
0.3%
6 420
0.3%
7 379
0.3%
8 433
0.3%
9 395
0.3%
10 386
0.3%
11 400
0.3%
12 383
0.3%
13 421
0.3%
14 393
0.3%
ValueCountFrequency (%)
61 692
0.5%
60 832
0.6%
59 812
0.5%
58 757
0.5%
57 823
0.6%
56 746
0.5%
55 798
0.5%
54 832
0.6%
53 787
0.5%
52 777
0.5%

Interactions

2024-06-11T08:23:41.064518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:26.367181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:27.864855image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.362673image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.880429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.397584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.795089image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.196834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.638781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.041557image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.622502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.206817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:26.487930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.003628image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.509363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.018990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.528982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.934151image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.328858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.773793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.181398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.754115image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.349277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:26.614066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.132150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.653165image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.150917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.659021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.066749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.451611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.911174image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.315713image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.880331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.489818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:26.741220image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.268682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.796816image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.287027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.786365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.193283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.655328image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.042407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.446204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.010243image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.614799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:26.870333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.400206image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.929288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.415483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.905238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.322086image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.770663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.167558image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.573104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.140077image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.743781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:26.999276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.525209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.059018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.623355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.030341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.444458image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.896272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.295260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.729930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.273186image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.863608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:27.121545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.659671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.190941image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.746114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.144803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.564274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.010961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.415470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:38.877271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.405704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:41.999356image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:27.256113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.796003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.313881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:31.868613image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.276607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.692590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.133802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.541564image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.013682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.540381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:42.127923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:27.461697image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:28.927538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.454860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.001783image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.402026image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.816624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.254064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.660050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.144362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.659785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:42.264453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:27.599120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.070826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.601735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.138001image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.539801image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:34.949285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.390103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.796796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.280859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.803288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:42.388803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:27.731401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:29.225765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:30.746965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:32.276194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:33.675106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:35.081946image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:36.521656image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:37.919240image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:39.414085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2024-06-11T08:23:40.945833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2024-06-11T08:23:54.776336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Credit_ScoreCredit_WorthinessGenderIDInterest_rate_spreadLTVNeg_ammortizationRegionSecured_bySecurity_TypeStatusUpfront_chargesageapprov_in_advbusiness_or_commercialco-applicant_credit_typeconstruction_typecredit_typedtir1incomeinterest_onlyloan_amountloan_limitloan_purposeloan_typelump_sum_paymentoccupancy_typeopen_creditproperty_valuerate_of_interestsubmission_of_applicationtermtotal_units
Credit_Score1.0000.0060.000-0.001-0.003-0.0040.0020.0000.0070.0070.005-0.0020.0040.0000.0000.0000.0070.003-0.0000.0010.0000.0050.0000.0070.0000.0000.0030.0000.005-0.0020.006-0.0050.003
Credit_Worthiness0.0061.0000.0040.0010.056-0.0120.0590.0050.0000.0000.035-0.0030.0100.0620.0010.0130.0000.0230.0160.0090.050-0.0220.0240.0500.0130.0310.0070.2300.0030.1450.022-0.0550.008
Gender0.0000.0041.0000.0030.0430.0200.0170.3760.0030.0030.0840.1180.0710.0120.0480.6650.0030.023-0.014-0.0260.012-0.0830.0390.0690.0800.0120.0200.022-0.089-0.0150.360-0.0610.013
ID-0.0010.0010.0031.0000.003-0.0050.0000.0000.0050.0050.004-0.0050.0000.0000.0000.0070.0050.005-0.0070.0040.006-0.0010.0000.0000.0020.0000.0000.0080.001-0.0000.002-0.0020.004
Interest_rate_spread-0.0030.0560.0430.0031.0000.1110.0370.0221.0001.0001.0000.1000.0340.0560.3850.0681.0000.0260.075-0.2510.013-0.4260.0550.1890.4120.0100.1280.058-0.4520.5830.299-0.1660.027
LTV-0.004-0.0120.020-0.0050.1111.0000.0000.0000.0000.0000.003-0.1150.0000.0030.0160.0040.0000.0000.156-0.0230.0000.0830.0080.0000.0100.0000.0000.000-0.3650.0300.0060.2100.000
Neg_ammortization0.0020.0590.0170.0000.0370.0001.0000.0070.0060.0060.1560.0190.0150.0780.0140.0100.0060.079-0.0230.0260.0180.0400.0080.0730.0190.0510.0310.0000.051-0.1730.0510.0740.014
Region0.0000.0050.3760.0000.0220.0000.0071.0000.0040.0040.0500.0580.0240.0080.0540.0300.0040.0150.0170.0080.0000.0060.0070.0470.0510.0130.0320.0090.025-0.0140.145-0.0450.006
Secured_by0.0070.0000.0030.0051.0000.0000.0060.0041.0000.9850.025NaN0.0040.0000.0060.0050.9850.003-0.003-0.0100.000-0.0110.0020.0040.0070.0050.0000.000-0.013NaN0.0000.0000.000
Security_Type0.0070.0000.0030.0051.0000.0000.0060.0040.9851.0000.025NaN0.0040.0000.0060.0050.9850.0030.0030.0100.0000.0110.0020.0040.0070.0050.0000.0000.013NaN0.000-0.0000.000
Status0.0050.0350.0840.0041.0000.0030.1560.0500.0250.0251.000-0.0230.0500.0370.0920.1440.0250.5920.096-0.1390.014-0.0690.0540.0400.0940.1880.0300.010-0.1080.0230.121-0.0170.029
Upfront_charges-0.002-0.0030.118-0.0050.100-0.1150.0190.058NaNNaN-0.0231.0000.0290.0050.1070.0341.0000.009-0.017-0.0800.000-0.1160.0750.0870.0840.0000.0261.000-0.075-0.0520.165-0.1160.037
age0.0040.0100.0710.0000.0340.0000.0150.0240.0040.0040.0500.0291.0000.0320.0790.0490.0040.0200.036-0.2020.007-0.2380.0320.1910.1080.0110.0600.040-0.0790.0310.270-0.0710.007
approv_in_adv0.0000.0620.0120.0000.0560.0030.0780.0080.0000.0000.0370.0050.0321.0000.0100.0120.0000.0180.014-0.0140.074-0.0300.0960.1540.0130.0610.0200.005-0.0620.0680.0810.0340.004
business_or_commercial0.0000.0010.0480.0000.3850.0160.0140.0540.0060.0060.0920.1070.0790.0101.0000.0230.0060.028-0.1740.2180.0070.1750.0220.0651.0000.0140.1110.0240.2860.0540.090-0.0950.026
co-applicant_credit_type0.0000.0130.6650.0070.0680.0040.0100.0300.0050.0050.1440.0340.0490.0120.0231.0000.0050.339-0.0520.2360.0140.1570.0410.0450.0500.0340.0260.0160.176-0.0450.062-0.0020.000
construction_type0.0070.0000.0030.0051.0000.0000.0060.0040.9850.9850.0251.0000.0040.0000.0060.0051.0000.0030.0030.0100.0000.0110.0020.0040.0070.0050.0000.0000.013NaN0.000-0.0000.000
credit_type0.0030.0230.0230.0050.0260.0000.0790.0150.0030.0030.5920.0090.0200.0180.0280.3390.0031.0000.008-0.0270.014-0.0160.0320.0390.0490.1210.0080.009-0.029-0.0210.0460.0070.000
dtir1-0.0000.016-0.014-0.0070.0750.156-0.0230.017-0.0030.0030.096-0.0170.0360.014-0.174-0.0520.0030.0081.000-0.3070.0040.0210.0340.0820.2620.0410.0390.013-0.0480.0620.0560.1080.014
income0.0010.009-0.0260.004-0.251-0.0230.0260.008-0.0100.010-0.139-0.080-0.202-0.0140.2180.2360.010-0.027-0.3071.0000.0000.6420.0470.0140.0100.0000.0440.0100.606-0.0900.020-0.0410.017
interest_only0.0000.0500.0120.0060.0130.0000.0180.0000.0000.0000.0140.0000.0070.0740.0070.0140.0000.0140.0040.0001.000-0.0040.0310.0220.0110.0330.0110.273-0.0430.0390.010-0.0090.003
loan_amount0.005-0.022-0.083-0.001-0.4260.0830.0400.006-0.0110.011-0.069-0.116-0.238-0.0300.1750.1570.011-0.0160.0210.642-0.0041.0000.4560.1020.1050.0070.0330.0300.857-0.1720.4110.1960.079
loan_limit0.0000.0240.0390.0000.0550.0080.0080.0070.0020.0020.0540.0750.0320.0960.0220.0410.0020.0320.0340.0470.0310.4561.0000.0410.0630.0190.0150.0180.1280.0390.0110.0070.008
loan_purpose0.0070.0500.0690.0000.1890.0000.0730.0470.0040.0040.0400.0870.1910.1540.0650.0450.0040.0390.0820.0140.0220.1020.0411.0000.0660.0160.1320.0830.201-0.3020.265-0.1230.017
loan_type0.0000.0130.0800.0020.4120.0100.0190.0510.0070.0070.0940.0840.1080.0131.0000.0500.0070.0490.2620.0100.0110.1050.0630.0661.0000.0140.1090.034-0.277-0.2440.1100.1360.028
lump_sum_payment0.0000.0310.0120.0000.0100.0000.0510.0130.0050.0050.1880.0000.0110.0610.0140.0340.0050.1210.0410.0000.0330.0070.0190.0160.0141.0000.0000.0040.0090.0150.013-0.0110.000
occupancy_type0.0030.0070.0200.0000.1280.0000.0310.0320.0000.0000.0300.0260.0600.0200.1110.0260.0000.0080.0390.0440.0110.0330.0150.1320.1090.0001.0000.014-0.044-0.1740.067-0.0150.169
open_credit0.0000.2300.0220.0080.0580.0000.0000.0090.0000.0000.0101.0000.0400.0050.0240.0160.0000.0090.0130.0100.2730.0300.0180.0830.0340.0040.0141.0000.0760.1040.0450.0260.005
property_value0.0050.003-0.0890.001-0.452-0.3650.0510.025-0.0130.013-0.108-0.075-0.079-0.0620.2860.1760.013-0.029-0.0480.606-0.0430.8570.1280.201-0.2770.009-0.0440.0761.000-0.1790.0390.0880.034
rate_of_interest-0.0020.145-0.015-0.0000.5830.030-0.173-0.014NaNNaN0.023-0.0520.0310.0680.054-0.045NaN-0.0210.062-0.0900.039-0.1720.039-0.302-0.2440.015-0.1740.104-0.1791.0000.1240.1910.058
submission_of_application0.0060.0220.3600.0020.2990.0060.0510.1450.0000.0000.1210.1650.2700.0810.0900.0620.0000.0460.0560.0200.0100.4110.0110.2650.1100.0130.0670.0450.0390.1241.000-0.1680.070
term-0.005-0.055-0.061-0.002-0.1660.2100.074-0.0450.000-0.000-0.017-0.116-0.0710.034-0.095-0.002-0.0000.0070.108-0.041-0.0090.1960.007-0.1230.136-0.011-0.0150.0260.0880.191-0.1681.0000.013
total_units0.0030.0080.0130.0040.0270.0000.0140.0060.0000.0000.0290.0370.0070.0040.0260.0000.0000.0000.0140.0170.0030.0790.0080.0170.0280.0000.1690.0050.0340.0580.0700.0131.000

Missing values

2024-06-11T08:23:42.717698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-11T08:23:43.445346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-11T08:23:44.383343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDyearloan_limitGenderapprov_in_advloan_typeloan_purposeCredit_Worthinessopen_creditbusiness_or_commercialloan_amountrate_of_interestInterest_rate_spreadUpfront_chargestermNeg_ammortizationinterest_onlylump_sum_paymentproperty_valueconstruction_typeoccupancy_typeSecured_bytotal_unitsincomecredit_typeCredit_Scoreco-applicant_credit_typeagesubmission_of_applicationLTVRegionSecurity_TypeStatusdtir1
0248902019cfSex Not Availablenopretype1p1l1nopcnob/c116500NaNNaNNaN360.0not_negnot_intnot_lpsm118000.0sbprhome1U1740.0EXP758CIB25-34to_inst98.728814southdirect145.0
1248912019cfMalenopretype2p1l1nopcb/c206500NaNNaNNaN360.0not_negnot_intlpsmNaNsbprhome1U4980.0EQUI552EXP55-64to_instNaNNorthdirect1NaN
2248922019cfMalepretype1p1l1nopcnob/c4065004.5600.2000595.00360.0neg_ammnot_intnot_lpsm508000.0sbprhome1U9480.0EXP834CIB35-44to_inst80.019685southdirect046.0
3248932019cfMalenopretype1p4l1nopcnob/c4565004.2500.6810NaN360.0not_negnot_intnot_lpsm658000.0sbprhome1U11880.0EXP587CIB45-54not_inst69.376900Northdirect042.0
4248942019cfJointpretype1p1l1nopcnob/c6965004.0000.30420.00360.0not_negnot_intnot_lpsm758000.0sbprhome1U10440.0CRIF602EXP25-34not_inst91.886544Northdirect039.0
5248952019cfJointpretype1p1l1nopcnob/c7065003.9900.1523370.00360.0not_negnot_intnot_lpsm1008000.0sbprhome1U10080.0EXP864EXP35-44not_inst70.089286Northdirect040.0
6248962019cfJointpretype1p3l1nopcnob/c3465004.5000.99985120.00360.0not_negnot_intnot_lpsm438000.0sbprhome1U5040.0EXP860EXP55-64to_inst79.109589Northdirect044.0
7248972019NaNFemalenopretype1p4l1nopcnob/c2665004.1250.29755609.88360.0not_negnot_intnot_lpsm308000.0sbprhome1U3780.0CIB863CIB55-64to_inst86.525974Northdirect042.0
8248982019cfJointnopretype1p3l1nopcnob/c3765004.8750.73951150.00360.0not_negnot_intnot_lpsm478000.0sbprhome1U5580.0CIB580EXP55-64to_inst78.765690centraldirect044.0
9248992019cfSex Not Availablenopretype3p3l1nopcnob/c4365003.490-0.27762316.50360.0not_negnot_intnot_lpsm688000.0sbprhome1U6720.0CIB788EXP55-64to_inst63.444767southdirect030.0
IDyearloan_limitGenderapprov_in_advloan_typeloan_purposeCredit_Worthinessopen_creditbusiness_or_commercialloan_amountrate_of_interestInterest_rate_spreadUpfront_chargestermNeg_ammortizationinterest_onlylump_sum_paymentproperty_valueconstruction_typeoccupancy_typeSecured_bytotal_unitsincomecredit_typeCredit_Scoreco-applicant_credit_typeagesubmission_of_applicationLTVRegionSecurity_TypeStatusdtir1
1486601735502019cfFemalenopretype1p4l1nopcnob/c3665003.875-0.11713643.16360.0not_negnot_intnot_lpsm658000.0sbprhome1U7200.0CIB851EXP45-54not_inst55.699088Northdirect020.0
1486611735512019cfSex Not Availablenopretype2p4l1nopcb/c346500NaNNaNNaN360.0not_negnot_intnot_lpsm358000.0sbprhome1UNaNEXP585CIB25-34to_inst96.787710southdirect1NaN
1486621735522019cfJointnopretype1p4l1nopcnob/c6465003.6250.07437639.80360.0not_negint_onlynot_lpsm828000.0sbprhome1U13500.0CIB873EXP45-54not_inst78.079710Northdirect031.0
1486631735532019cfMalenopretype2p1l1nopcb/c106500NaNNaNNaN360.0not_negnot_intnot_lpsmNaNsbprhome1U1860.0EQUI619EXP<25to_instNaNNorthdirect1NaN
1486641735542019cfJointnopretype2p1l1nopcb/c1565003.9901.40153113.06360.0not_negnot_intnot_lpsm158000.0sbprhome1U4020.0EXP859EXP65-74to_inst99.050633centraldirect045.0
1486651735552019cfSex Not Availablenopretype1p3l1nopcnob/c4365003.1250.25719960.00180.0not_negnot_intnot_lpsm608000.0sbprhome1U7860.0CIB659EXP55-64to_inst71.792763southdirect048.0
1486661735562019cfMalenopretype1p1l1nopcnob/c5865005.1900.85440.00360.0not_negnot_intnot_lpsm788000.0sbirhome4U7140.0CIB569CIB25-34not_inst74.428934southdirect015.0
1486671735572019cfMalenopretype1p4l1nopcnob/c4465003.1250.08161226.64180.0not_negnot_intnot_lpsm728000.0sbprhome1U6900.0CIB702EXP45-54not_inst61.332418Northdirect049.0
1486681735582019cfFemalenopretype1p4l1nopcnob/c1965003.5000.58244323.33180.0not_negnot_intnot_lpsm278000.0sbprhome1U7140.0EXP737EXP55-64to_inst70.683453Northdirect029.0
1486691735592019cfFemalenopretype1p3l1nopcnob/c4065004.3751.38716000.00240.0not_negnot_intnot_lpsm558000.0sbprhome1U7260.0CIB830CIB45-54not_inst72.849462Northdirect044.0